YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository.

Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team.

YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN).

The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.

You can use the raw model for object detection. See the model hub to look for all available YOLOS models.

Here is how to use this model:

Currently, both the feature extractor and model support PyTorch.

The YOLOS model was pre-trained on ImageNet-1k and fine-tuned on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively.

The model was pre-trained for 200 epochs on ImageNet-1k and fine-tuned for 150 epochs on COCO.

This model achieves an AP (average precision) of 36.1 on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.